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    ABSTRACT: In this study, a new approach to the palmprint recognition phase is presented. 2D Gabor filters are used for feature extraction of palmprints. After Gabor filtering, standard deviations are computed in order to generate the palmprint feature vector. Genetic Algorithm-based feature selection is used to select the best feature subset from the palmprint feature set. An Artificial Neural Network (ANN) based on hybrid algorithm combining Particle Swarm Optimization (PSO) algorithm with back-propagation algorithms has been applied to the selected feature vectors for recognition of the persons. Network architecture and connection weights of ANN are evolved by a PSO method, and then, the appropriate network architecture and connection weights are fed into ANN. Recognition rate equal to 96% is obtained by using conjugate gradient descent algorithm.
    Neural Computing and Applications 22(1). · 1.76 Impact Factor
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    ABSTRACT: This paper presents a bimodal biometric recognition system based on the extracted features of the human palmprint and iris using a new graph-based approach termed Fisher locality preserving projections (FLPP). This new technique employs two graphs with the first being used to characterize the within-class compactness and the second dedicated to the augmentation of the between-class separability. By applying the FLPP, only the most discriminant and stable palmprint and iris features are retained. FLPP was implemented on the frequency domain by transforming the extracted region of interest extraction of both biometric modalities using Fourier transform. Subsequently, the palmprint and iris features vectors obtained are matched with their counterpart in the templates databases and the obtained scores are fused to produce a final decision. The proposed combination of palmprint and iris patterns has shown an excellent performance compared to unimodal palmprint biometric recognition. The system was evaluated on a database of 108 subjects and the experimental results show that our system performs very well and achieves a high accuracy expressed by an equal error rate of 0.00%.
    Journal of Real-Time Image Processing 09/2013; · 1.16 Impact Factor
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    ABSTRACT: This paper employs both two-dimensional (2D) and three-dimensional (3D) features of palmprint for recognition. While 2D palmprint image contains plenty of texture information, 3D palmprint image contains the depth information of the palm surface. Using two different features, we can achieve higher recognition accuracy than using only one of them. In addition, we can improve the robustness. To recognize palmprints, we use two-phase test sample representation (TPTSR) which is proved to be successful in face recognition. Before TPTSR, we perform principal component analysis to extract global features from the 2D and 3D palmprint images. We make decision based on the fusion of 2D and 3D features matching scores. We perform experiments on the PolyU 2D + 3D palmprint database which contains 8,000 samples and achieve satisfying recognition performance.
    Neural Computing and Applications 03/2014; · 1.76 Impact Factor

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